The following acronyms are used in this specification:
ECG—electrocardiogramSSFP—steady state free-precessionEEG—electroencephalogramMHE—magneto-hydrodynamic effectEEG—electromyogramSVD—singular value decompositionMR—magnetic resonanceGMF—gradient magnetic fieldABP—arterial blood pressureEMI—electromagnetic interferenceA/D—analog-to-digitalEMC—electromagnetic compatibilityDSP—digital signal processingCMR—cardiovascular magneticresonance
Physiological monitoring has become an essential part of health and disease management. A number of monitoring modalities, sensors and systems have been developed for various settings and patient groups. They include in-hospital monitoring systems (e.g., bedside monitors and systems for patient monitoring during surgeries and other medical procedures), as well as out-of-hospital (ambulatory) and home monitoring systems. The most common types of collected information are electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), temperature, respiration (breathing) rate and amplitude, oxygen saturation (pulse oximetry), arterial blood pressure, glucose, hemoglobin, physical activity, vascular resistance and cardiac output.
Majority of in-hospital monitoring systems collect data from multiple sensors and/or channels. For example, cardiovascular hemodynamic monitoring often include 12-lead ECG, 4 blood-pressure and pulse-oximetry channels; the cardiac electrophysiological monitoring systems include at least 8 surface ECG channels and additional channels for collecting intracardiac electrograms, whereas the EEG monitoring systems may incorporate up to 100 channels.
Because most physiological signals are relatively small, quire frequent data sampling and real-time data transmission, both electromagnetic interference and wireless data transmission represent major challenges for the development of such monitoring systems, as detailed below.
I. Electromagnetic Interference
Powerful sources of electromagnetic interference that are usually present in a modern hospital environment can generate substantial amount of noise, distortion and interference. Magnetic-resonance (MR) scanners is an example of a powerful source of electromagnetic interference, which can lead to signal artifacts that are several-orders of magnitude greater than ECG or EEG signals. This interference becomes particularly important due to the requirement for high-fidelity, diagnostic ECG monitoring during interventional cardiovascular MR procedures and tracking subtle changes in the amplitude of electrocardiographic ST-segment and T-wave, which may signal the earliest signs of ischemia in patients with coronary artery disease. Moreover, the interventional cardiovascular MR procedures also require fast patient transportation (with continuous monitoring) from the MR-scanner room to and X-ray room and back. Due to the requirement of continuous monitoring during both procedures, as well during transportation between the two rooms, a single, wireless system must be used for this setting. The frequency of the signals generated by MR-scanner's gradient magnetic fields (“GMF-interference”) often overlaps with the frequency of cardiac signals (“true ECG”). In this situation, ECG signals represent a combination of the true ECG and GMF-interference. Because the magnitude of MR-gradients (GMF) is usually several orders of magnitude greater than the magnitude of the true ECG, the MR-contaminated signals require substantial filtering, which modifies the pattern of the cardiac signals and diminishes its diagnostic value. In addition, the patterns of ECG signals in the presence of strong magnetic fields are changed by the magneto-hydrodynamic effect [MHE], which arises due to the circulation of magnetized blood in subject's body. Although a number of filtering and reconstruction approaches have been developed to address this issue, an accurate, high-fidelity reconstruction of the diagnostic quality true-ECG signal remains an open challenge. (Wu V. et. al. J Adaptive Noise Cancellation to Suppress Electrocardiography Artifacts During Real-time Interventional MRI. Magnetic Resonance Imaging, 33(5):1184-93. (2011).
The prior-art ECG reconstruction methods can be divided into three groups:                A. Approaches utilizing MR-gradient signals, which are either obtained directly from the MR-scanner or its control equipment, Odille et al. Noise Cancellation Signal Processing Method and Computer System for Improved. Real-time Electrocardiogram Artifact Correction During MRI data Acquisition, IEEE Trans Biomed Eng, 54(4) pp. 630-40 (2007); additional “blanking” can be employed for preventing saturation of ECG amplifiers during the time periods of changes in MR-gradients, which induce large voltages in the ECG sensing cascades; Tse et al. A 1.5T MRI-Conditional 12-Lead Electrocardiogram for MRI and Intra-MR Intervention; Magnetic Resonance in Medicine 71 pp 1336-1347 (2014);        B. Methods utilizing dedicated, external antennas (coils, loops) for detecting changes in electromagnetic fields induced by MR-gradients (Laudon et al. Minimizing interference from magnetic resonance imagers during electrocardiography. IEEE Trans Biomed Eng., 45(2) pp 160-4 (1998); Felblinger et al. Restoration of Electrophysiological Signals Distorted by Inductive Effects of Magnetic Field Gradients During MR Sequences, Magnetic Resonance in Medicine, 41 pp 715-21 (1999); and        C. Approaches based on modeling ECG signals, using the signals obtained outside the MR-scanner, and relying on a simplified assumption that the ECG waveforms do not change during subsequent MR-scanning (Oster J, et. al. Nonlinear Bayesian Filtering for Denoising of Electrocardiograms Acquired in a Magnetic Resonance Environment IEEE Transactions on Biomedical Engineering. Vol. 57 No. 7, pp 1628-38 (July 2010).        
II. Wireless Communication
Wireless connectivity offers mobility and convenience, which cannot be achieved using “wire-based” systems. In a hospital setting, this allows uninterrupted patient monitoring and movement of patients between different procedure/surgery rooms, intensive care units, emergency rooms and hospital beds. In an out-of-hospital or home setting, wireless systems allow continuous monitoring during sleep and daily activities; they can also be used on the road and in other settings.
However, wireless data transmission poses several challenges compared with the wire-based systems. First, the speed and rate of wireless data transmission are limited. This creates significant problems for the development of multi-channel/multi-sensor wireless systems, which require significant data throughput (such as cardiac electrophysiological systems, cardiac hemodynamic monitoring or EEG-mapping systems). Furthermore, wireless systems are susceptible to electromagnetic noise and interference from external sources. This issue is particularly important for medical monitoring in the emergency setting and during interventional procedures, where uninterrupted, high-fidelity, real-time data are essential for patient diagnosis and management. Rapid proliferation of medical equipment with powerful electromagnetic sources (e.g., magnetic-resonance (MR) scanners, X-ray machines, etc.) makes this issue particularly challenging in the modern hospital environment. Practically, this leads to the necessity to change the patient monitoring systems when a patient is moved for different diagnostic procedures and treatment throughout a hospital. This requires detachment and re-attachment of multiple ECG leads and other sensors to the patient, adding burden of time, effort and cost for medical institutions and creating discontinuities (gaps) in patient monitoring.
Traditionally, wireless radio-frequency transmitters have been viewed as a simple replacement for wire-based data transmission. Therefore, the wireless system designs have essentially copied the wire-based systems and added a single radio-frequency transmitter/receiver (Bluetooth, WiFi, Zigbee, cell-phone, etc.). However, as explained above, this design strategy can lead to several problems. Specifically, a single radio-frequency transmitter has a limited data throughput, which may not be sufficient for multi-channel, high-sampling-rate data monitoring. Furthermore, wireless communication, using a single transmitter, can be significantly affected or completely interrupted by external electromagnetic interference, which may seriously complicate patient management and outcomes in the emergency settings. Any transmission errors, delay or interruptions in this situation may be life-threatening and lead to delayed or inappropriate medical response. This problem becomes even more difficult when the distance between the wireless radio transmitter and receiver changes during the transmission (for example, when the patient is being transported between two different procedure rooms, while the data are being transmitted wirelessly to a “control room” where physicians/nurses monitor the data in real time).